The use of genetic programming for adaptive text compression

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  title =        "The use of genetic programming for adaptive text
  author =       "M. Zaki and M. Sayed",
  year =         "2009",
  month =        mar # "~24",
  volume =       "1",
  journal =      "International Journal of Information and Coding
  pages =        "88--108",
  keywords =     "genetic algorithms, genetic programming, Huffman code,
                 adaptive text compression, data compression, lossless
                 compression, alphabet, Arabic language",
  ISSN =         "1753-7711",
  DOI =          "doi:10.1504/IJICOT.2009.024048",
  bibsource =    "OAI-PMH server at",
  language =     "eng",
  URL =          "",
  publisher =    "Inderscience Publishers",
  abstract =     "This paper exploits a modified genetic programming
                 (GP) approach for solving the data compression problem.
                 In fact, the typical GP algorithm in which a candidate
                 solution is expressed as a tree rather than a bit
                 string, fails to solve that problem since it does not
                 guarantee a one to one correspondence between a
                 particular symbol and the corresponding codeword during
                 subtree exchange operations. The nature of the problem
                 requires generating one, and only one, codeword for
                 each symbol of the underlying text. In the proposed
                 scheme, the authors introduced three new operators,
                 namely, insertion, two-level mutation and modified
                 crossover. Accordingly, a modified version of GP is
                 presented and applied on different data texts to
                 validate the proposed approach. The developed algorithm
                 can provide optimum codes since its final solution
                 reaches Huffman tree. Moreover, it makes use of GP not
                 only to allow optimum compression ratio but also to
                 provide adaptive compression implementation. The
                 adaptation is achieved so that the selection of the
                 codebook depends on the nature of the input text. The
                 proposed compression scheme is written in C++ and is
                 implemented on different text types under various
                 operational conditions. Accordingly, the algorithm
                 performance has been measured and evaluated.",

Genetic Programming entries for M Zaki M Sayed